SOTAVerified

Automatic Speech Recognition (ASR)

Automatic Speech Recognition (ASR) involves converting spoken language into written text. It is designed to transcribe spoken words into text in real-time, allowing people to communicate with computers, mobile devices, and other technology using their voice. The goal of Automatic Speech Recognition is to accurately transcribe speech, taking into account variations in accent, pronunciation, and speaking style, as well as background noise and other factors that can affect speech quality.

Papers

Showing 476500 of 3012 papers

TitleStatusHype
ed-cec: improving rare word recognition using asr postprocessing based on error detection and context-aware error correctionCode0
Effects of Layer Freezing on Transferring a Speech Recognition System to Under-resourced LanguagesCode0
Adversarial Training For Low-Resource Disfluency CorrectionCode0
DoCIA: An Online Document-Level Context Incorporation Agent for Speech TranslationCode0
Are Neural Open-Domain Dialog Systems Robust to Speech Recognition Errors in the Dialog History? An Empirical StudyCode0
Does Joint Training Really Help Cascaded Speech Translation?Code0
Arabic Speech Recognition by End-to-End, Modular Systems and HumanCode0
Discrete Cross-Modal Alignment Enables Zero-Shot Speech TranslationCode0
Speech Emotion Recognition with ASR Transcripts: A Comprehensive Study on Word Error Rate and Fusion TechniquesCode0
Arabic Dysarthric Speech Recognition Using Adversarial and Signal-Based AugmentationCode0
Discrete Speech Unit Extraction via Independent Component AnalysisCode0
Domain Specific Wav2vec 2.0 Fine-tuning For The SE&R 2022 ChallengeCode0
Did you hear that? Adversarial Examples Against Automatic Speech RecognitionCode0
A Comparative Study on Transformer vs RNN in Speech ApplicationsCode0
A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature ExtractorsCode0
A Dataset for Speech Emotion Recognition in Greek Theatrical PlaysCode0
SSR7000: A Synchronized Corpus of Ultrasound Tongue Imaging for End-to-End Silent Speech RecognitionCode0
Direct Segmentation Models for Streaming Speech TranslationCode0
BERT Attends the Conversation: Improving Low-Resource Conversational ASRCode0
Detecting Adversarial Examples for Speech Recognition via Uncertainty QuantificationCode0
Deep Learning for Audio Signal ProcessingCode0
Deep Spiking Neural Networks for Large Vocabulary Automatic Speech RecognitionCode0
DiaCorrect: End-to-end error correction for speaker diarizationCode0
DISCO: A Large Scale Human Annotated Corpus for Disfluency Correction in Indo-European LanguagesCode0
Data Fusion for Audiovisual Speaker Localization: Extending Dynamic Stream Weights to the Spatial DomainCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TM-CTCTest WER10.1Unverified
2TM-seq2seqTest WER9.7Unverified
3CTC/attentionTest WER8.2Unverified
4LF-MMI TDNNTest WER6.7Unverified
5Whisper-LLaMATest WER6.6Unverified
6End2end ConformerTest WER3.9Unverified
7End2end ConformerTest WER3.7Unverified
8MoCo + wav2vec (w/o extLM)Test WER2.7Unverified
9CTC/AttentionTest WER1.5Unverified
10WhisperTest WER1.3Unverified
#ModelMetricClaimedVerifiedStatus
1SpatialNetCER14.5Unverified
2CleanMel-L-maskCER14.4Unverified
#ModelMetricClaimedVerifiedStatus
1ConformerTest WER15.32Unverified
2Whisper-largev3-finetunedTest WER10.82Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)1.89Unverified
#ModelMetricClaimedVerifiedStatus
1DistillAVWER1.4Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)4.28Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)8.04Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)3.36Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer Transducer (German)WER (%)8.98Unverified